Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import concatenate\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from pandas import concat\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import os\n",
    "dataPath = '../PedestrianData/IntentionData'\n",
    "finalPath = '../PedestrianData/final.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir(dataPath)\n",
    "final = []\n",
    "for i in range(len(files)):\n",
    "    file = dataPath + \"/\" + files[i]\n",
    "    \n",
    "    # Reading trial csv to headers\n",
    "    df = pd.read_csv(file, sep=\",\", header=None, \n",
    "                 names=[\"Time\", \n",
    "                        \"Pos_x\", \"Pos_y\", \"Pos_z\", \n",
    "                        \"Gaz_x\", \"Gaz_y\", \"Gaz_z\", \n",
    "                        \"Vel_x\", \"Vel_z\",\n",
    "                        \"Acc_x\", \"Acc_z\",\n",
    "                        \"Vel_Mag\", \"Acc_Mag\",\n",
    "                        \"Vel_Ang\", \"Acc_Ang\", \"Gaz_Ang\",\n",
    "                        \"Vel_Bin\", \"Acc_Bin\", \"Gaz_Bin\", \"Chg_Bin\"          \n",
    "                       ])\n",
    "    \n",
    "    \n",
    "    final.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = final[0]\n",
    "dataset.drop(\"Time\", axis = 1, inplace = True)\n",
    "dataset.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "values = dataset.values\n",
    "values = values.astype('float32')\n",
    "# specify columns to plot\n",
    "groups = [0, 1, 2, 3, 5, 6, 7]\n",
    "i = 1\n",
    "# plot each column\n",
    "pyplot.figure()\n",
    "for group in groups:\n",
    "    pyplot.subplot(len(groups), 1, i)\n",
    "    pyplot.plot(values[:, group])\n",
    "    pyplot.title(dataset.columns[group], y=0.5, loc='right')\n",
    "    i += 1\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize features\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(values)\n",
    "\n",
    "# frame as supervised learning\n",
    "reframed = series_to_supervised(values, 5, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var2(t-5)</th>\n",
       "      <th>var3(t-5)</th>\n",
       "      <th>var4(t-5)</th>\n",
       "      <th>var5(t-5)</th>\n",
       "      <th>var6(t-5)</th>\n",
       "      <th>var7(t-5)</th>\n",
       "      <th>var8(t-5)</th>\n",
       "      <th>var9(t-5)</th>\n",
       "      <th>var10(t-5)</th>\n",
       "      <th>var11(t-5)</th>\n",
       "      <th>var12(t-5)</th>\n",
       "      <th>var13(t-5)</th>\n",
       "      <th>var14(t-5)</th>\n",
       "      <th>var15(t-5)</th>\n",
       "      <th>var16(t-5)</th>\n",
       "      <th>var17(t-5)</th>\n",
       "      <th>var18(t-5)</th>\n",
       "      <th>var19(t-5)</th>\n",
       "      <th>var1(t-4)</th>\n",
       "      <th>var2(t-4)</th>\n",
       "      <th>var3(t-4)</th>\n",
       "      <th>var4(t-4)</th>\n",
       "      <th>var5(t-4)</th>\n",
       "      <th>var6(t-4)</th>\n",
       "      <th>var7(t-4)</th>\n",
       "      <th>var8(t-4)</th>\n",
       "      <th>var9(t-4)</th>\n",
       "      <th>var10(t-4)</th>\n",
       "      <th>var11(t-4)</th>\n",
       "      <th>var12(t-4)</th>\n",
       "      <th>var13(t-4)</th>\n",
       "      <th>var14(t-4)</th>\n",
       "      <th>var15(t-4)</th>\n",
       "      <th>var16(t-4)</th>\n",
       "      <th>var17(t-4)</th>\n",
       "      <th>var18(t-4)</th>\n",
       "      <th>var19(t-4)</th>\n",
       "      <th>var1(t-3)</th>\n",
       "      <th>var2(t-3)</th>\n",
       "      <th>var3(t-3)</th>\n",
       "      <th>var4(t-3)</th>\n",
       "      <th>var5(t-3)</th>\n",
       "      <th>var6(t-3)</th>\n",
       "      <th>var7(t-3)</th>\n",
       "      <th>var8(t-3)</th>\n",
       "      <th>var9(t-3)</th>\n",
       "      <th>var10(t-3)</th>\n",
       "      <th>var11(t-3)</th>\n",
       "      <th>var12(t-3)</th>\n",
       "      <th>var13(t-3)</th>\n",
       "      <th>var14(t-3)</th>\n",
       "      <th>var15(t-3)</th>\n",
       "      <th>var16(t-3)</th>\n",
       "      <th>var17(t-3)</th>\n",
       "      <th>var18(t-3)</th>\n",
       "      <th>var19(t-3)</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var2(t-2)</th>\n",
       "      <th>var3(t-2)</th>\n",
       "      <th>var4(t-2)</th>\n",
       "      <th>var5(t-2)</th>\n",
       "      <th>var6(t-2)</th>\n",
       "      <th>var7(t-2)</th>\n",
       "      <th>var8(t-2)</th>\n",
       "      <th>var9(t-2)</th>\n",
       "      <th>var10(t-2)</th>\n",
       "      <th>var11(t-2)</th>\n",
       "      <th>var12(t-2)</th>\n",
       "      <th>var13(t-2)</th>\n",
       "      <th>var14(t-2)</th>\n",
       "      <th>var15(t-2)</th>\n",
       "      <th>var16(t-2)</th>\n",
       "      <th>var17(t-2)</th>\n",
       "      <th>var18(t-2)</th>\n",
       "      <th>var19(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var3(t-1)</th>\n",
       "      <th>var4(t-1)</th>\n",
       "      <th>var5(t-1)</th>\n",
       "      <th>var6(t-1)</th>\n",
       "      <th>var7(t-1)</th>\n",
       "      <th>var8(t-1)</th>\n",
       "      <th>var9(t-1)</th>\n",
       "      <th>var10(t-1)</th>\n",
       "      <th>var11(t-1)</th>\n",
       "      <th>var12(t-1)</th>\n",
       "      <th>var13(t-1)</th>\n",
       "      <th>var14(t-1)</th>\n",
       "      <th>var15(t-1)</th>\n",
       "      <th>var16(t-1)</th>\n",
       "      <th>var17(t-1)</th>\n",
       "      <th>var18(t-1)</th>\n",
       "      <th>var19(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
       "      <th>var6(t)</th>\n",
       "      <th>var7(t)</th>\n",
       "      <th>var8(t)</th>\n",
       "      <th>var9(t)</th>\n",
       "      <th>var10(t)</th>\n",
       "      <th>var11(t)</th>\n",
       "      <th>var12(t)</th>\n",
       "      <th>var13(t)</th>\n",
       "      <th>var14(t)</th>\n",
       "      <th>var15(t)</th>\n",
       "      <th>var16(t)</th>\n",
       "      <th>var17(t)</th>\n",
       "      <th>var18(t)</th>\n",
       "      <th>var19(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.945389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.904137</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.174638</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.904137</td>\n",
       "      <td>8.174638</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.889563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.913230</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>7.913230</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.945389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.904137</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.174638</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.904137</td>\n",
       "      <td>8.174638</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.889563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.913230</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>7.913230</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.904137</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.174638</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.904137</td>\n",
       "      <td>8.174638</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.889563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.913230</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>7.913230</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212.005386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.126746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.889563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.913230</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>7.913230</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212.005386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>198.434952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.037506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.889563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.91323</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>7.913230</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212.005386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>198.434952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>186.340195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.889563</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.91323</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>7.913230</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>233.130096</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.10122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.005386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>198.434952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>186.340195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.10122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.101220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.005386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.434952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>186.340195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.996195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.924036</td>\n",
       "      <td>1.408832</td>\n",
       "      <td>9.924036</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>171.869904</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.005386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.434952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186.340195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.996195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.924036</td>\n",
       "      <td>1.408832</td>\n",
       "      <td>9.924036</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>171.869904</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.085747</td>\n",
       "      <td>8.957859</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>12.067416</td>\n",
       "      <td>90.0</td>\n",
       "      <td>42.070759</td>\n",
       "      <td>161.565048</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198.434952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186.340195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.996195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.924036</td>\n",
       "      <td>1.408832</td>\n",
       "      <td>9.924036</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>171.869904</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.085747</td>\n",
       "      <td>8.957859</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>12.067416</td>\n",
       "      <td>90.0</td>\n",
       "      <td>42.070759</td>\n",
       "      <td>161.565048</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.104841</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.104841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>158.198593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186.340195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.8</td>\n",
       "      <td>653.900024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>289.899994</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.996195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.924036</td>\n",
       "      <td>1.408832</td>\n",
       "      <td>9.924036</td>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>171.869904</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.085747</td>\n",
       "      <td>8.957859</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>12.067416</td>\n",
       "      <td>90.0</td>\n",
       "      <td>42.070759</td>\n",
       "      <td>161.565048</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.104841</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.104841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>158.198593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>1.7</td>\n",
       "      <td>653.799988</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>158.198593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     var1(t-5)  var2(t-5)   var3(t-5)  var4(t-5)  var5(t-5)  var6(t-5)  \\\n",
       "5   290.000000        1.8  654.000000       -0.8       -0.2       -0.6   \n",
       "6   290.000000        1.8  654.000000       -0.9       -0.2       -0.5   \n",
       "7   289.899994        1.8  654.000000       -0.9       -0.1       -0.4   \n",
       "8   289.899994        1.8  654.000000       -0.9       -0.2       -0.4   \n",
       "9   289.899994        1.8  654.000000       -0.9       -0.2       -0.4   \n",
       "10  289.899994        1.8  653.900024       -0.8       -0.2       -0.6   \n",
       "11  289.899994        1.8  653.900024       -0.7       -0.2       -0.7   \n",
       "12  289.899994        1.8  653.900024       -0.5       -0.3       -0.8   \n",
       "13  289.899994        1.8  653.900024       -0.3       -0.3       -0.9   \n",
       "14  289.899994        1.8  653.900024       -0.1       -0.4       -0.9   \n",
       "\n",
       "    var7(t-5)  var8(t-5)  var9(t-5)  var10(t-5)  var11(t-5)  var12(t-5)  \\\n",
       "5    0.000000   0.000000   0.000000     0.00000    0.000000    0.000000   \n",
       "6    0.000000   0.000000   0.000000     0.00000    0.000000    0.000000   \n",
       "7   -0.904137   0.000000  -8.174638     0.00000    0.904137    8.174638   \n",
       "8    0.000000   0.000000   8.126746     0.00000    0.000000    8.126746   \n",
       "9    0.000000   0.000000   0.000000     0.00000    0.000000    0.000000   \n",
       "10   0.000000  -0.889563   0.000000    -7.91323    0.889563    7.913230   \n",
       "11   0.000000   0.000000   0.000000     8.10122    0.000000    8.101220   \n",
       "12   0.000000   0.000000   0.000000     0.00000    0.000000    0.000000   \n",
       "13   0.000000   0.000000   0.000000     0.00000    0.000000    0.000000   \n",
       "14   0.000000   0.000000   0.000000     0.00000    0.000000    0.000000   \n",
       "\n",
       "    var13(t-5)  var14(t-5)  var15(t-5)  var16(t-5)  var17(t-5)  var18(t-5)  \\\n",
       "5          0.0         0.0  233.130096         0.0         0.0         6.0   \n",
       "6          0.0         0.0  240.945389         0.0         0.0         6.0   \n",
       "7        270.0       270.0  246.037506         7.0         7.0         6.0   \n",
       "8          0.0        90.0  246.037506         0.0         3.0         6.0   \n",
       "9          0.0         0.0  246.037506         0.0         0.0         6.0   \n",
       "10       180.0       180.0  233.130096         5.0         5.0         6.0   \n",
       "11         0.0         0.0  225.000000         0.0         1.0         6.0   \n",
       "12         0.0         0.0  212.005386         0.0         0.0         5.0   \n",
       "13         0.0         0.0  198.434952         0.0         0.0         5.0   \n",
       "14         0.0         0.0  186.340195         0.0         0.0         5.0   \n",
       "\n",
       "    var19(t-5)   var1(t-4)  var2(t-4)   var3(t-4)  var4(t-4)  var5(t-4)  \\\n",
       "5          6.0  290.000000        1.8  654.000000       -0.9       -0.2   \n",
       "6          4.0  289.899994        1.8  654.000000       -0.9       -0.1   \n",
       "7          4.0  289.899994        1.8  654.000000       -0.9       -0.2   \n",
       "8          3.0  289.899994        1.8  654.000000       -0.9       -0.2   \n",
       "9          3.0  289.899994        1.8  653.900024       -0.8       -0.2   \n",
       "10         2.0  289.899994        1.8  653.900024       -0.7       -0.2   \n",
       "11         3.0  289.899994        1.8  653.900024       -0.5       -0.3   \n",
       "12         2.0  289.899994        1.8  653.900024       -0.3       -0.3   \n",
       "13         2.0  289.899994        1.8  653.900024       -0.1       -0.4   \n",
       "14         2.0  289.899994        1.8  653.900024        0.0       -0.5   \n",
       "\n",
       "    var6(t-4)  var7(t-4)  var8(t-4)  var9(t-4)  var10(t-4)  var11(t-4)  \\\n",
       "5        -0.5   0.000000   0.000000   0.000000     0.00000    0.000000   \n",
       "6        -0.4  -0.904137   0.000000  -8.174638     0.00000    0.904137   \n",
       "7        -0.4   0.000000   0.000000   8.126746     0.00000    0.000000   \n",
       "8        -0.4   0.000000   0.000000   0.000000     0.00000    0.000000   \n",
       "9        -0.6   0.000000  -0.889563   0.000000    -7.91323    0.889563   \n",
       "10       -0.7   0.000000   0.000000   0.000000     8.10122    0.000000   \n",
       "11       -0.8   0.000000   0.000000   0.000000     0.00000    0.000000   \n",
       "12       -0.9   0.000000   0.000000   0.000000     0.00000    0.000000   \n",
       "13       -0.9   0.000000   0.000000   0.000000     0.00000    0.000000   \n",
       "14       -0.9   0.000000   0.000000   0.000000     0.00000    0.000000   \n",
       "\n",
       "    var12(t-4)  var13(t-4)  var14(t-4)  var15(t-4)  var16(t-4)  var17(t-4)  \\\n",
       "5     0.000000         0.0         0.0  240.945389         0.0         0.0   \n",
       "6     8.174638       270.0       270.0  246.037506         7.0         7.0   \n",
       "7     8.126746         0.0        90.0  246.037506         0.0         3.0   \n",
       "8     0.000000         0.0         0.0  246.037506         0.0         0.0   \n",
       "9     7.913230       180.0       180.0  233.130096         5.0         5.0   \n",
       "10    8.101220         0.0         0.0  225.000000         0.0         1.0   \n",
       "11    0.000000         0.0         0.0  212.005386         0.0         0.0   \n",
       "12    0.000000         0.0         0.0  198.434952         0.0         0.0   \n",
       "13    0.000000         0.0         0.0  186.340195         0.0         0.0   \n",
       "14    0.000000         0.0         0.0  180.000000         0.0         0.0   \n",
       "\n",
       "    var18(t-4)  var19(t-4)   var1(t-3)  var2(t-3)   var3(t-3)  var4(t-3)  \\\n",
       "5          6.0         4.0  289.899994        1.8  654.000000       -0.9   \n",
       "6          6.0         4.0  289.899994        1.8  654.000000       -0.9   \n",
       "7          6.0         3.0  289.899994        1.8  654.000000       -0.9   \n",
       "8          6.0         3.0  289.899994        1.8  653.900024       -0.8   \n",
       "9          6.0         2.0  289.899994        1.8  653.900024       -0.7   \n",
       "10         6.0         3.0  289.899994        1.8  653.900024       -0.5   \n",
       "11         5.0         2.0  289.899994        1.8  653.900024       -0.3   \n",
       "12         5.0         2.0  289.899994        1.8  653.900024       -0.1   \n",
       "13         5.0         2.0  289.899994        1.8  653.900024        0.0   \n",
       "14         5.0         3.0  289.899994        1.7  653.799988        0.1   \n",
       "\n",
       "    var5(t-3)  var6(t-3)  var7(t-3)  var8(t-3)  var9(t-3)  var10(t-3)  \\\n",
       "5        -0.1       -0.4  -0.904137   0.000000  -8.174638    0.000000   \n",
       "6        -0.2       -0.4   0.000000   0.000000   8.126746    0.000000   \n",
       "7        -0.2       -0.4   0.000000   0.000000   0.000000    0.000000   \n",
       "8        -0.2       -0.6   0.000000  -0.889563   0.000000   -7.913230   \n",
       "9        -0.2       -0.7   0.000000   0.000000   0.000000    8.101220   \n",
       "10       -0.3       -0.8   0.000000   0.000000   0.000000    0.000000   \n",
       "11       -0.3       -0.9   0.000000   0.000000   0.000000    0.000000   \n",
       "12       -0.4       -0.9   0.000000   0.000000   0.000000    0.000000   \n",
       "13       -0.5       -0.9   0.000000   0.000000   0.000000    0.000000   \n",
       "14       -0.7       -0.7   0.000000  -0.996195   0.000000   -9.924036   \n",
       "\n",
       "    var11(t-3)  var12(t-3)  var13(t-3)  var14(t-3)  var15(t-3)  var16(t-3)  \\\n",
       "5     0.904137    8.174638       270.0       270.0  246.037506         7.0   \n",
       "6     0.000000    8.126746         0.0        90.0  246.037506         0.0   \n",
       "7     0.000000    0.000000         0.0         0.0  246.037506         0.0   \n",
       "8     0.889563    7.913230       180.0       180.0  233.130096         5.0   \n",
       "9     0.000000    8.101220         0.0         0.0  225.000000         0.0   \n",
       "10    0.000000    0.000000         0.0         0.0  212.005386         0.0   \n",
       "11    0.000000    0.000000         0.0         0.0  198.434952         0.0   \n",
       "12    0.000000    0.000000         0.0         0.0  186.340195         0.0   \n",
       "13    0.000000    0.000000         0.0         0.0  180.000000         0.0   \n",
       "14    1.408832    9.924036       180.0       180.0  171.869904         5.0   \n",
       "\n",
       "    var17(t-3)  var18(t-3)  var19(t-3)   var1(t-2)  var2(t-2)   var3(t-2)  \\\n",
       "5          7.0         6.0         4.0  289.899994        1.8  654.000000   \n",
       "6          3.0         6.0         3.0  289.899994        1.8  654.000000   \n",
       "7          0.0         6.0         3.0  289.899994        1.8  653.900024   \n",
       "8          5.0         6.0         2.0  289.899994        1.8  653.900024   \n",
       "9          1.0         6.0         3.0  289.899994        1.8  653.900024   \n",
       "10         0.0         5.0         2.0  289.899994        1.8  653.900024   \n",
       "11         0.0         5.0         2.0  289.899994        1.8  653.900024   \n",
       "12         0.0         5.0         2.0  289.899994        1.8  653.900024   \n",
       "13         0.0         5.0         3.0  289.899994        1.7  653.799988   \n",
       "14         5.0         4.0         3.0  290.000000        1.7  653.799988   \n",
       "\n",
       "    var4(t-2)  var5(t-2)  var6(t-2)  var7(t-2)  var8(t-2)  var9(t-2)  \\\n",
       "5        -0.9       -0.2       -0.4   0.000000   0.000000   8.126746   \n",
       "6        -0.9       -0.2       -0.4   0.000000   0.000000   0.000000   \n",
       "7        -0.8       -0.2       -0.6   0.000000  -0.889563   0.000000   \n",
       "8        -0.7       -0.2       -0.7   0.000000   0.000000   0.000000   \n",
       "9        -0.5       -0.3       -0.8   0.000000   0.000000   0.000000   \n",
       "10       -0.3       -0.3       -0.9   0.000000   0.000000   0.000000   \n",
       "11       -0.1       -0.4       -0.9   0.000000   0.000000   0.000000   \n",
       "12        0.0       -0.5       -0.9   0.000000   0.000000   0.000000   \n",
       "13        0.1       -0.7       -0.7   0.000000  -0.996195   0.000000   \n",
       "14        0.2       -0.8       -0.6   0.899208   0.000000   8.085747   \n",
       "\n",
       "    var10(t-2)  var11(t-2)  var12(t-2)  var13(t-2)  var14(t-2)  var15(t-2)  \\\n",
       "5     0.000000    0.000000    8.126746         0.0   90.000000  246.037506   \n",
       "6     0.000000    0.000000    0.000000         0.0    0.000000  246.037506   \n",
       "7    -7.913230    0.889563    7.913230       180.0  180.000000  233.130096   \n",
       "8     8.101220    0.000000    8.101220         0.0    0.000000  225.000000   \n",
       "9     0.000000    0.000000    0.000000         0.0    0.000000  212.005386   \n",
       "10    0.000000    0.000000    0.000000         0.0    0.000000  198.434952   \n",
       "11    0.000000    0.000000    0.000000         0.0    0.000000  186.340195   \n",
       "12    0.000000    0.000000    0.000000         0.0    0.000000  180.000000   \n",
       "13   -9.924036    1.408832    9.924036       180.0  180.000000  171.869904   \n",
       "14    8.957859    0.899208   12.067416        90.0   42.070759  161.565048   \n",
       "\n",
       "    var16(t-2)  var17(t-2)  var18(t-2)  var19(t-2)   var1(t-1)  var2(t-1)  \\\n",
       "5          0.0         3.0         6.0         3.0  289.899994        1.8   \n",
       "6          0.0         0.0         6.0         3.0  289.899994        1.8   \n",
       "7          5.0         5.0         6.0         2.0  289.899994        1.8   \n",
       "8          0.0         1.0         6.0         3.0  289.899994        1.8   \n",
       "9          0.0         0.0         5.0         2.0  289.899994        1.8   \n",
       "10         0.0         0.0         5.0         2.0  289.899994        1.8   \n",
       "11         0.0         0.0         5.0         2.0  289.899994        1.8   \n",
       "12         0.0         0.0         5.0         3.0  289.899994        1.7   \n",
       "13         5.0         5.0         4.0         3.0  290.000000        1.7   \n",
       "14         3.0         1.0         4.0         2.0  290.000000        1.7   \n",
       "\n",
       "     var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  var7(t-1)  var8(t-1)  \\\n",
       "5   654.000000       -0.9       -0.2       -0.4   0.000000   0.000000   \n",
       "6   653.900024       -0.8       -0.2       -0.6   0.000000  -0.889563   \n",
       "7   653.900024       -0.7       -0.2       -0.7   0.000000   0.000000   \n",
       "8   653.900024       -0.5       -0.3       -0.8   0.000000   0.000000   \n",
       "9   653.900024       -0.3       -0.3       -0.9   0.000000   0.000000   \n",
       "10  653.900024       -0.1       -0.4       -0.9   0.000000   0.000000   \n",
       "11  653.900024        0.0       -0.5       -0.9   0.000000   0.000000   \n",
       "12  653.799988        0.1       -0.7       -0.7   0.000000  -0.996195   \n",
       "13  653.799988        0.2       -0.8       -0.6   0.899208   0.000000   \n",
       "14  653.799988        0.2       -0.8       -0.5   0.000000   0.000000   \n",
       "\n",
       "    var9(t-1)  var10(t-1)  var11(t-1)  var12(t-1)  var13(t-1)  var14(t-1)  \\\n",
       "5    0.000000    0.000000    0.000000    0.000000         0.0    0.000000   \n",
       "6    0.000000   -7.913230    0.889563    7.913230       180.0  180.000000   \n",
       "7    0.000000    8.101220    0.000000    8.101220         0.0    0.000000   \n",
       "8    0.000000    0.000000    0.000000    0.000000         0.0    0.000000   \n",
       "9    0.000000    0.000000    0.000000    0.000000         0.0    0.000000   \n",
       "10   0.000000    0.000000    0.000000    0.000000         0.0    0.000000   \n",
       "11   0.000000    0.000000    0.000000    0.000000         0.0    0.000000   \n",
       "12   0.000000   -9.924036    1.408832    9.924036       180.0  180.000000   \n",
       "13   8.085747    8.957859    0.899208   12.067416        90.0   42.070759   \n",
       "14  -8.104841    0.000000    0.000000    8.104841         0.0  270.000000   \n",
       "\n",
       "    var15(t-1)  var16(t-1)  var17(t-1)  var18(t-1)  var19(t-1)     var1(t)  \\\n",
       "5   246.037506         0.0         0.0         6.0         3.0  289.899994   \n",
       "6   233.130096         5.0         5.0         6.0         2.0  289.899994   \n",
       "7   225.000000         0.0         1.0         6.0         3.0  289.899994   \n",
       "8   212.005386         0.0         0.0         5.0         2.0  289.899994   \n",
       "9   198.434952         0.0         0.0         5.0         2.0  289.899994   \n",
       "10  186.340195         0.0         0.0         5.0         2.0  289.899994   \n",
       "11  180.000000         0.0         0.0         5.0         3.0  289.899994   \n",
       "12  171.869904         5.0         5.0         4.0         3.0  290.000000   \n",
       "13  161.565048         3.0         1.0         4.0         2.0  290.000000   \n",
       "14  158.198593         0.0         7.0         4.0         3.0  290.000000   \n",
       "\n",
       "    var2(t)     var3(t)  var4(t)  var5(t)  var6(t)   var7(t)   var8(t)  \\\n",
       "5       1.8  653.900024     -0.8     -0.2     -0.6  0.000000 -0.889563   \n",
       "6       1.8  653.900024     -0.7     -0.2     -0.7  0.000000  0.000000   \n",
       "7       1.8  653.900024     -0.5     -0.3     -0.8  0.000000  0.000000   \n",
       "8       1.8  653.900024     -0.3     -0.3     -0.9  0.000000  0.000000   \n",
       "9       1.8  653.900024     -0.1     -0.4     -0.9  0.000000  0.000000   \n",
       "10      1.8  653.900024      0.0     -0.5     -0.9  0.000000  0.000000   \n",
       "11      1.7  653.799988      0.1     -0.7     -0.7  0.000000 -0.996195   \n",
       "12      1.7  653.799988      0.2     -0.8     -0.6  0.899208  0.000000   \n",
       "13      1.7  653.799988      0.2     -0.8     -0.5  0.000000  0.000000   \n",
       "14      1.7  653.799988      0.2     -0.8     -0.5  0.000000  0.000000   \n",
       "\n",
       "     var9(t)  var10(t)  var11(t)   var12(t)  var13(t)    var14(t)    var15(t)  \\\n",
       "5   0.000000 -7.913230  0.889563   7.913230     180.0  180.000000  233.130096   \n",
       "6   0.000000  8.101220  0.000000   8.101220       0.0    0.000000  225.000000   \n",
       "7   0.000000  0.000000  0.000000   0.000000       0.0    0.000000  212.005386   \n",
       "8   0.000000  0.000000  0.000000   0.000000       0.0    0.000000  198.434952   \n",
       "9   0.000000  0.000000  0.000000   0.000000       0.0    0.000000  186.340195   \n",
       "10  0.000000  0.000000  0.000000   0.000000       0.0    0.000000  180.000000   \n",
       "11  0.000000 -9.924036  1.408832   9.924036     180.0  180.000000  171.869904   \n",
       "12  8.085747  8.957859  0.899208  12.067416      90.0   42.070759  161.565048   \n",
       "13 -8.104841  0.000000  0.000000   8.104841       0.0  270.000000  158.198593   \n",
       "14  0.000000  0.000000  0.000000   0.000000       0.0    0.000000  158.198593   \n",
       "\n",
       "    var16(t)  var17(t)  var18(t)  var19(t)  \n",
       "5        5.0       5.0       6.0       2.0  \n",
       "6        0.0       1.0       6.0       3.0  \n",
       "7        0.0       0.0       5.0       2.0  \n",
       "8        0.0       0.0       5.0       2.0  \n",
       "9        0.0       0.0       5.0       2.0  \n",
       "10       0.0       0.0       5.0       3.0  \n",
       "11       5.0       5.0       4.0       3.0  \n",
       "12       3.0       1.0       4.0       2.0  \n",
       "13       0.0       7.0       4.0       3.0  \n",
       "14       0.0       0.0       4.0       3.0  "
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "pd.options.display.max_columns = None\n",
    "reframed.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 85) 100 (100,)\n",
      "(100, 5, 17) (100,) (127, 5, 17) (127,)\n"
     ]
    }
   ],
   "source": [
    "n_steps = 5\n",
    "n_features = 17\n",
    "\n",
    "# split into train and test sets\n",
    "values = reframed.values\n",
    "n_timesteps_train = 100\n",
    "train = values[:n_timesteps_train, :]\n",
    "test = values[n_timesteps_train:, :]\n",
    "# split into input and outputs\n",
    "n_obs = n_steps * n_features\n",
    "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n",
    "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n",
    "print(train_X.shape, len(train_X), train_y.shape)\n",
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], n_steps, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], n_steps, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 100 samples, validate on 127 samples\n",
      "Epoch 1/1000\n",
      "100/100 [==============================] - 3s 30ms/step - loss: 651.4157 - val_loss: 646.6624\n",
      "Epoch 2/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 645.8766 - val_loss: 639.6172\n",
      "Epoch 3/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 636.6653 - val_loss: 628.9277\n",
      "Epoch 4/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 623.8548 - val_loss: 614.8193\n",
      "Epoch 5/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 607.4378 - val_loss: 597.1959\n",
      "Epoch 6/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 587.4230 - val_loss: 576.1230\n",
      "Epoch 7/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 563.8850 - val_loss: 551.6613\n",
      "Epoch 8/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 536.9145 - val_loss: 523.8033\n",
      "Epoch 9/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 506.4474 - val_loss: 492.5804\n",
      "Epoch 10/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 472.7444 - val_loss: 458.4456\n",
      "Epoch 11/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 435.9611 - val_loss: 421.2811\n",
      "Epoch 12/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 396.1276 - val_loss: 381.1850\n",
      "Epoch 13/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 353.3339 - val_loss: 338.2825\n",
      "Epoch 14/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 307.3143 - val_loss: 291.3476\n",
      "Epoch 15/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 258.3652 - val_loss: 242.7703\n",
      "Epoch 16/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 206.9957 - val_loss: 191.5892\n",
      "Epoch 17/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 152.9754 - val_loss: 137.8689\n",
      "Epoch 18/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 96.3709 - val_loss: 81.6737\n",
      "Epoch 19/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 37.2448 - val_loss: 24.1608\n",
      "Epoch 20/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.5969 - val_loss: 23.0457\n",
      "Epoch 21/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.2237 - val_loss: 23.0612\n",
      "Epoch 22/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.3365 - val_loss: 22.8472\n",
      "Epoch 23/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.1931 - val_loss: 22.7787\n",
      "Epoch 24/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.1634 - val_loss: 22.6196\n",
      "Epoch 25/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.1009 - val_loss: 22.5156\n",
      "Epoch 26/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.0730 - val_loss: 22.4067\n",
      "Epoch 27/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.0998 - val_loss: 22.3230\n",
      "Epoch 28/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.1444 - val_loss: 22.1863\n",
      "Epoch 29/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.9994 - val_loss: 22.0819\n",
      "Epoch 30/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.0297 - val_loss: 22.0645\n",
      "Epoch 31/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.0771 - val_loss: 21.9248\n",
      "Epoch 32/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.9315 - val_loss: 21.8344\n",
      "Epoch 33/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.9345 - val_loss: 21.8251\n",
      "Epoch 34/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.0307 - val_loss: 21.5662\n",
      "Epoch 35/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.9216 - val_loss: 21.4666\n",
      "Epoch 36/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.8909 - val_loss: 21.4576\n",
      "Epoch 37/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.9617 - val_loss: 21.2486\n",
      "Epoch 38/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.8298 - val_loss: 21.1794\n",
      "Epoch 39/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 4.0954 - val_loss: 21.2527\n",
      "Epoch 40/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.8970 - val_loss: 21.0471\n",
      "Epoch 41/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.7666 - val_loss: 21.0805\n",
      "Epoch 42/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.7588 - val_loss: 21.0637\n",
      "Epoch 43/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.7508 - val_loss: 20.8719\n",
      "Epoch 44/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.6899 - val_loss: 20.8213\n",
      "Epoch 45/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.7156 - val_loss: 20.7287\n",
      "Epoch 46/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.7740 - val_loss: 20.4364\n",
      "Epoch 47/1000\n",
      "100/100 [==============================] - 0s 4ms/step - loss: 3.6826 - val_loss: 20.3875\n",
      "Epoch 48/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.6209 - val_loss: 20.3366\n",
      "Epoch 49/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.6546 - val_loss: 20.2500\n",
      "Epoch 50/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.6379 - val_loss: 20.1696\n",
      "Epoch 51/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.6614 - val_loss: 20.0612\n",
      "Epoch 52/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.6114 - val_loss: 19.9630\n",
      "Epoch 53/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.5621 - val_loss: 19.8894\n",
      "Epoch 54/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.4979 - val_loss: 19.8106\n",
      "Epoch 55/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.4725 - val_loss: 19.7676\n",
      "Epoch 56/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.4697 - val_loss: 19.6903\n",
      "Epoch 57/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.4459 - val_loss: 19.6137\n",
      "Epoch 58/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.4304 - val_loss: 19.5679\n",
      "Epoch 59/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.3953 - val_loss: 19.5018\n",
      "Epoch 60/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.3667 - val_loss: 19.4310\n",
      "Epoch 61/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.3449 - val_loss: 19.3906\n",
      "Epoch 62/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.3606 - val_loss: 19.3286\n",
      "Epoch 63/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.3008 - val_loss: 19.2990\n",
      "Epoch 64/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.3196 - val_loss: 19.2317\n",
      "Epoch 65/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.2419 - val_loss: 19.1872\n",
      "Epoch 66/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.2510 - val_loss: 19.1516\n",
      "Epoch 67/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.2352 - val_loss: 19.1007\n",
      "Epoch 68/1000\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 3.2010 - val_loss: 19.0673\n",
      "Epoch 69/1000\n",
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     ]
    },
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     "text": [
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     ]
    },
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    },
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    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "Epoch 928/1000\n",
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      "Epoch 937/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 0s 4ms/step - loss: 0.1283 - val_loss: 15.6845\n",
      "Epoch 938/1000\n",
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      "Epoch 999/1000\n",
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      "Epoch 1000/1000\n",
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     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=1000, batch_size=1, validation_data=(test_X, test_y), verbose=1, shuffle=False)\n",
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 38.554\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "\n",
    "# make a prediction\n",
    "yhat = model.predict(test_X)\n",
    "test_X = test_X.reshape((test_X.shape[0], n_steps*n_features))\n",
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)\n",
    "#inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,0]\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_y, test_X[:, -7:]), axis=1)\n",
    "#inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,0]\n",
    "# calculate RMSE\n",
    "rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n",
    "print('Test RMSE: %.3f' % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 78 is out of bounds for axis 1 with size 5",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-305-00a088e867ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_X\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m78\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_X\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m78\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'g'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_X\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m78\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myhat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 78 is out of bounds for axis 1 with size 5"
     ]
    }
   ],
   "source": [
    "plt.plot(train_X[:,78], train_y, 'b')\n",
    "\n",
    "plt.plot(test_X[:,78], test_y, 'g')\n",
    "plt.plot(test_X[:,78], yhat, 'r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "           0.       ,   0.       ],\n",
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       "           0.       , 240.94539  ],\n",
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       "          -0.8895634,   0.       ]],\n",
       "\n",
       "       [[289.9      ,   1.8      , 654.       , ..., 246.0375   ,\n",
       "           7.       ,   7.       ],\n",
       "        [  6.       ,   4.       , 289.9      , ...,   0.       ,\n",
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       "\n",
       "       ...,\n",
       "\n",
       "       [[289.9      ,   1.8      , 653.9      , ..., 135.       ,\n",
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       "        [  4.       ,   2.       , 289.9      , ...,   0.       ,\n",
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       "\n",
       "       [[289.9      ,   1.8      , 653.9      , ..., 119.0546   ,\n",
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      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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